VesiclePy Segmentation Model Training and Performance Metrics
by Jason Ken Adhinarta·Updated 22d ago
5.5 KB1files
Available on 1 platform
Sign in to view source links and access this dataset
Description
A 5.5 KB Excel dataset contains performance metrics for VesiclePy, an automated pipeline for segmenting and classifying neuronal vesicles from Volume Electron Microscopy data. The dataset, created by Jason Ken Adhinarta and last updated in May 2026, includes results from processing a multi-terabyte serial EM dataset of Hydra vulgaris, annotating 53,851 vesicles from 20 complete neurons.
Use Cases
Benchmarking segmentation model performance based on ground truth manual annotations mentioned in the description
Analyzing vesicle classification accuracy for 5 distinct vesicle types identified by the pipeline
Evaluating the spatial analysis capabilities of VesiclePy based on unique 3D location data for each vesicle
Clustering neurons into subtypes based on combined vesicle data and morphological information
Strengths
Includes ground truth manual annotations for quantifying performance
Covers 53,851 vesicles from 20 complete neurons
Classifies vesicles into 5 distinct types
Limitations
Column-level documentation is absent; field semantics must be inferred after download
Row count is unknown, which may limit suitability assessment
The dataset is very small (5.5 KB), suggesting it contains summary metrics rather than raw data
Provenance
Source
figshare
Collection Method
Generated by the VesiclePy pipeline using high-pressure frozen serial EM data of Hydra vulgaris.
Freshness
Last updated 2026-05-14 17:51:52; freshness should be verified